high performance computing on graphics processing units: hgpu.org

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Architecture-Adaptive Code Variant Tuning

Saurav Muralidharan, Amit Roy, Mary Hall, Michael Garland, Piyush Rai

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 750, nVidia GeForce GTX 770, nVidia GeForce GTX 980, Package, Performance, Tesla C2075, Tesla K20

May 21, 2016 by hgpu

Employing Directive Based Compression Solutions on Accelerators Global Memory under OpenACC

Ebad Salehi

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Tags: Compression, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Tesla K20, Thesis

May 21, 2016 by hgpu

Exposing Errors Related to Weak Memory in GPU Applications

Tyler Sorensen, Alastair F. Donaldson

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Tags: Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 770, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, nVidia Quadro K5200, Tesla C2050, Tesla C2075, Tesla K20

May 3, 2016 by hgpu

Adaptive GPU Array Layout Auto-Tuning

Nicolas Weber, Michael Goesele

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Tags: Computer science, CUDA, Linear Algbera, nVidia, nVidia GeForce GTX Titan X, Package, Performance, Tesla K20

April 29, 2016 by hgpu

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

Benjamin Worpitz

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Tags: Computer science, CUDA, Intel Xeon Phi, OpenMP, Package, Particle-in-cell methods, Performance, performance portability, Tesla K20, Tesla K80, Thesis

April 26, 2016 by hgpu

GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server

Henggang Cui, Hao Zhang, Gregory R. Ganger, Phillip B. Gibbons, Eric P. Xing

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Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, Tesla K20

April 16, 2016 by hgpu

Portable and Transparent Software Managed Scheduling on Accelerators for Fair Resource Sharing

Christos Margiolas, Michael F.P. O'Boyle

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Tags: AMD Radeon R9 295X2, ATI, Computer science, nVidia, OpenCL, Performance, Task scheduling, Tesla K20

April 12, 2016 by hgpu

GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing

X. Bellekens, C. Tachtatzis, R. C. Atkinson, C. Renfrew, T. Kirkham

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Tags: CUDA, nVidia, Security, Tesla K20

April 9, 2016 by xhgpu

A Highly-Efficient Memory-Compression Scheme for GPU-Accelerated Intrusion Detection Systems

X. J. A. Bellekens, C. Tachtatzis, R. C. Atkinson, C. Renfrew, T. Kirkham

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Tags: CUDA, nVidia, Security, Tesla K20

April 9, 2016 by xhgpu

Dynamic Sparse-Matrix Allocation on GPUs

James King, Thomas Gilray, Robert M. Kirby, Matthew Might

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Tags: Benchmarking, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse matrix, Tesla K20

April 3, 2016 by hgpu

Automatic Detection and Denoising of Signals in Large Geophysical Datasets

Gabriel O. Trisca

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Tags: Aparapi, Computer science, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan, OpenCL, RNN, Seismology, Sensing, Signal denoising, Tesla K20, Thesis

March 20, 2016 by hgpu

DySel: Lightweight Dynamic Selection for Kernel-based Data-parallel Programming Model

Li-Wen Chang, Hee-Seok Kim, Wen-mei Hwu

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, OpenCL, Performance, Tesla K20

March 15, 2016 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Architecture-Adaptive Code Variant Tuning

Employing Directive Based Compression Solutions on Accelerators Global Memory under OpenACC

Exposing Errors Related to Weak Memory in GPU Applications

Adaptive GPU Array Layout Auto-Tuning

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server

Portable and Transparent Software Managed Scheduling on Accelerators for Fair Resource Sharing

GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing

A Highly-Efficient Memory-Compression Scheme for GPU-Accelerated Intrusion Detection Systems

Dynamic Sparse-Matrix Allocation on GPUs

Automatic Detection and Denoising of Signals in Large Geophysical Datasets

DySel: Lightweight Dynamic Selection for Kernel-based Data-parallel Programming Model

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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